Study on optimal scheduling method of wind-photovoltaic-hydroelectric storage joint power generation system
With the increasing penetration rate of distributed energy in micro-grid,the micro-grid scheduling strategy based on discrete action space has some problems,such as low precision,poor optimization effect and poor stability.Based on this,a continuous action optimization scheduling solution method based on deep deterministic policy gradient(DDPG)algorithm is proposed.By establishing state space,action space,and reward function,the scheduling problem is transformed into a reinforcement learning problem,and solved by DDPG algorithm.The experimental results show that compared with the traditional deep Q-network(DQN)algorithm,the DDPG algorithm performs better in the convergence and accuracy of the reward function curve,and the scheduling strategy is also better.In terms of optimization objectives,the DDPG algorithm reduces the comprehensive operating cost and pollutant emissions of micro-grids by about 5％and 34％,respectively.
wind power generationphotovoltaic power generationhydroelectric power generationenergy storage batteryoptimized schedulingmulti-objective optimization
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